GIS

GIS

1. Applications

   • examples
     • Planning Environmental resource management Engineering Coastal defence Forest management Hydrology Epidemiology Emergency services Military Highways management
     • Water facilities management Telecoms Location-based services Population and geodemographic analysis Market analysis Community information and participation Academic
     • Mapping, Management, Engagement
   • Science or Systems?
     • GISystems began in 1960s as IT approaches to handling large volumes of spatial data
     • GIScience is ‘the discipline that uses geographic information systems as tools to understand the world
     • GIScience already existed prior to GISystems: it is the science of exploring and explaining spatial patterning. These tasks have traditionally been analogue

2. References to memorise

   • Khun, 2012
     • 10 Conceptrs
   • Michael Goodchild
   • Tobler, 1970
     • Near objects are more related
   • Kwan (2012a,b)
     • UGCoP
   • Snow, 1854
     • Analysis linked deaths of >500 individuals to a contaminated water pump.
   • Clarke, 1997
   • Wright et al., 1997
   • Bian, 2007
   • Nobel et al, 2006

3. 10 core concepts of GIS

   • Location - How one locates things, that is, what ground and what spatial relation one chooses, depends on context - location is a relation
   • Neighbourhood - Close things are more related to things far apart, companion concept to location - neighbourhoods remain imprecisely defined and lack crisp boundaries.
   • Field - Fields describe phenomena that have a scalar or vector attribute everywhere in a space of interest, for example, air temperatures on the Earth's surface - What is here? - more fundamental than objects - e.g. Lidar: elevations, pollution values
   • Objects - individuals that have an identity as well as attributes measure in fields - represented using a vector based model e.g. polygons, space outside this boundarry has no relation to the object and is nothing
   • Network - captures binary relationships between numbers of objects, roads, communication lines, blood vessels
   • Granularity - information that answers questions about the precision of spatial information,
   • Accuracy - answers questions about the correctness of spatial information. 2 assumptions: that there is a correct value or concept to compare to and understanding of measurement as a random process
   • Meaning - how to interpret the terms used in spatial information, saying what random numbers and figures mean by adding context, ontologies state constraints on the use and interpretation of terms.
   • Value - Roles played by spatial information in society/ the real world, assessing relation of spatial information to other values in society such as privacy, trust, infrastructure, or heritage

4. Conceptualising Spatial Entities

   • Error and Uncertainty
     • Accuracy & Precision
       • Root Mean Square Error - measure of accuracy, e.g. LIDAR datasets contain 3cm vertical accuracy
   • Spatial Analysis
     • UGCoP - Uncertain Geographical Context Problem, relating the information gathered correctly to the answer - e.g. tracking Cholera cases to a water tap rather than other places such as work environments (Snow, 1854)
     • What is it, where is it, what is its relation to other objects or entities

5. Bian's 8 types of spatial objects

   • Mobile objects - Individuals existing in small scale, clear boundaries, mobile.
   • Sedentary individuals - Individuals with clear boundaries, but fixed in space.
   • Masses of individuals - Individuals are identifiable but are either small in size or large in quantity (or both). They have collective behaviour.
   • Regions of individuals - Spatial regions extracted from the masses of individuals. They are typically seen as objects, but can contain individuals also perceived as objects.
   • Continuous solid mass - Large spatial extent and is continuous. Attributes vary across space (e.g. landsurfaces)
   • Continuous fluid mass - Phenomena which are spatially extensive, continuous, spatially variable and mobile (e.g. water, air)
   • Sedentary regions in mass - Regions which are extracted out of either solid or fluid masses. The regions are immobile (e.g. average locations used). (e.g. river catchments or stable pollution zones)
   • Mobile regions in mass - Mobile regions (e.g. pollution plumes, weather fronts)

6. Scale

   • MAUP - The Modifiable Area Unit Problem occurs when randomly defined boundaries are used for the reporting of spatial phenomena. e.g. population data uses individual people, but data is reported in areas (counties) in which the borders are arbitrary in a statistical sense and subject to change.
     • Choropleth Mapping - putting numbers or colour in areas, say theres 4 people in an area lable it as 4, but halfing it different ways can mean theres 2&2 or 1&3 people in each half - the number is generalising. two people in a vertical half are employed and the other two arent, but halved horizontally there could be employed people in each half - not equalling 50% of the 4 people being employed
       • Linear colour scheme doesn't work if there are more people in a rural area but more spaced out, the colour will still be darker because more people
         • quantify the population density so the darker values apply to higher densities
     • Election - more votes for one side could be changed due to the way the areas are divided
     • Crime maps, voting, population variables,poverty - the majority of deprived people do not live in deprived areas
     • Ecological Fallacy - assumptions of a area made on the individual people in that area e,g, Devon's average salary is £24,300, but it would be egologically fallacy to assume Farmer Giles makes £24,300
     • Selecting map threshold criteria
   • “A branch of philosophy that considers the existence of things in the world. It studies the generic traits of every mode of being and becoming, as well as the peculiar features of the major genera of existence” Bian (2007) p.268
     • Translation for GIS: the way in which we conceptualise geographic entities and model them within the digital environment. The ontology of a stream should include its relationships with other objects, its behaviour, and the way it is visualised. For individual objects, the ontology is how we choose to represent them best within the GIS.
   • Averaging removed variation and outliers in our data
   • UK Coastline - 200m scale creates a longer coastline than a 2000m scale
   • Gerymaderinging - elections
   • behaviourl choices affect results - e.g. getting illnes from cycling to work everyday is not linked to where you live or work

7. Interpolation

   • Toblers Law
     • Everything is related to everything else, but near things are more related than those far apart
     • What if the law is not true ? -There would be infinite local variability -The world would be unpredicatable -Every spatial environment would in essence be chaotic
       • (Goodchild, 2004)
     • Nearness? Distance can be measured in many ways (e.g. euclidian distance, time, travel cost, social distance) so proximity and nearness are terms that require explicit definition for the question at hand.
     • Miller, 2004
   • Definition
     • Collecting appropriate field data to to turn into data models of the spatial variation in the variables
     • “Spatial interpolation is a process of intelligent guesswork, in which the investigator (and the GIS) attempt to make a reasonable estimate of the value of a field at places where the field has not actually been measured”.
       • Longley et al, 2001
     • “Spatial interpolation is the procedure of estimating the values of properties at unsampled sites within an areas covered by existing values”.
       • Heywood et al, 2006
   • High density of observations: most methods will produce similar results - Sparse data: different methods may produce fairly different interpolations
   • Interpolation = Trend + weighted function of distance between measured points
   • Spline
     • Regularized - Creates a smooth, gradually changing surface with values that are likely to lie outside the range of the original dataset
     • Tension - Tunes the stiffness of the surface to reflect the character of the original data. Creates a less smooth surface
   • Kriging - Geostatistics
     • Kriging is an advanced geostatistical procedure that generates an estimated surface from a scattered set of points with z-values.
     • Kriging attempts to incorporate Tobler’s first law intelligently, by examining: (a) how ‘near’ is near? (and how far is too far?) (b) how related are points?
     • Pairs of points taken from a set of data points, pairs close to each other should have similar values compared to pairs far away
       • When these near pairs are potted on a grid there should be a line of best fit known as a semivariogram (1/2 squared difference between a pair of points plotted against h - distance between pair of points)
         • gradual increase at start followed by values plateauing at the end and the line not passing through origin
     • Points can be assigned a weighting gained from the shape of the variogram
     • Variogram trends: Spherical, Exponetial, Linear, Gaussian
     • As distance increases, the response variable becomes less predictable and are less related. But for closer things, the response is more predictable and has less variability.
   • IDW - Inverse Distance Weighting
     • Uses toblers law to estimate weighting for points - nearer the points greater the weighting
     • Positives: Smooth surface created - value at any point is like the value of nearby points
     • Negatives: May produce counterintuitive results which are visible in peaks and pits of a graph